Risk Classification of IP Addresses Using Machine Learning with Weighted Voting Approach - Nobre, Francisco V. J.; Alves, Davi O.; Araujo, Ramon S.; Campos, Gustavo A.; Gomes, Rafael L. (Anais do Latin-American Symposium on Dependable Computing (LADC); 2025: Anais do XIV Latin-American Symposium on Dependable Computing, 2025)
The growing complexity of cyberattacks demands intelligent and adaptive security solutions, one of which is the detection of malicious IP addresses. This paper presents a novel approach for classifying IP addresses by integrating Machine Learning (ML) with data from multiple public threat intelligence databases, where a novel dataset was built. The proposed solution applies a weighted voting mechanism to enhance interpretability and robustness by combining diverse data sources through a multi-criteria weighting strategy. The experimental results, in a real network environment, indicate that the solution enables scalable and automated risk classification of IP addresses.